Uncertainty Quantification in Estimating Critical Spacecraft Component Temperatures

Abstract

A method for quantifying uncertainty in conceptual-level design via a computationally- efficient probabilistic method is presented. The investigated method is applied to estimating the maximum-expected temperature of several critical components on a spacecraft. The variables of the design are first classified and assigned appropriate probability density functions. To characterize the thermal control system of the spacecraft, Subset Simulation, an efficient simulation technique originally developed for reliability analysis of civil engineering structures, is used. The results of Subset Simulation are compared with traditional Monte Carlo simulation. The investigated method allows uncertainty in the maximum-expected temperatures to be quantified based on the risk tolerance of the decision maker. For the spacecraft thermal control problem presented, Subset Simulation successfully replicated Monte Carlo simulation results for estimating the maximum-expected temperatures of several critical components yet required significantly less computational effort, in particular for risk-averse decision makers. Nomenclature